Resolving nanostructure and chemistry of solid-electrolyte interphase on lithium anodes by depth-sensitive plasmon-enhanced Raman spectroscopy.

The solid-electrolyte interphase (SEI) plays crucial roles for the reversible operation of lithium metal batteries. However, fundamental understanding of the mechanisms of SEI formation and evolution is still limited. Herein, we develop a depth-sensitive plasmon-enhanced Raman spectroscopy (DS-PERS) method to enable in-situ and nondestructive characterization of the nanostructure and chemistry of SEI, based on synergistic enhancements of localized surface plasmons from nanostructured Cu, shell-isolated Au nanoparticles and Li deposits at different depths. We monitor the sequential formation of SEI in both ether-based and carbonate-based dual-salt electrolytes on a Cu current collector and then on freshly deposited Li, with dramatic chemical reconstruction. The molecular-level insights from the DS-PERS study unravel the profound influences of Li in modifying SEI formation and in turn the roles of SEI in regulating the Li-ion desolvation and the subsequent Li deposition at SEI-coupled interfaces. Last, we develop a cycling protocol that promotes a favorable direct SEI formation route, which significantly enhances the performance of anode-free Li metal batteries.

Antepartum sleep quality, mental status, and postpartum depressive symptoms: a mediation analysis.

BACKGROUND: Poor sleep quality and maternal mood disturbances are common during pregnancy and may play pivotal roles in the development of postpartum depression. We aim to examine the trajectories of sleep quality and mental health in women from early pregnancy to delivery and explore the mediating effects of sleep quality and mental status on the link between antepartum depressive symptoms and postpartum depressive symptoms. METHODS: In an ongoing prospective birth cohort, 1301 women completed questionnaires in the first, second and third trimesters and at 6 weeks postpartum. In each trimester, sleep quality was measured utilizing the Pittsburgh Sleep Quality Index (PSQI), and mental health was assessed with the Center for Epidemiologic Studies Depression Scale (CES-D), the Self-Rating Anxiety Scale (SAS) and the Perceived Stress Scale (PSS). Postpartum depressive symptoms were evaluated by the Edinburgh Postnatal Depression Scale (EPDS). The bootstrap method was used to test the mediation effect. RESULTS: The PSQI, CES-D, and SAS scores presented U-shaped curves across the antenatal period while the PSS score followed a descending trend. Antenatal sleep quality, depressive symptoms, anxiety symptoms and perceived stress all predicted depressive symptoms at 6 weeks postpartum. The influence of antepartum depressive symptoms on postpartum depressive symptoms was mediated by antepartum sleep quality and anxiety symptoms, which accounted for 32.14%, 39.25% and 31.25% in the first, second and third trimesters (P = 0.002, P = 0.001, P = 0.001, respectively). CONCLUSIONS: Poor sleep quality and anxiety symptoms in pregnancy mediated the relationship between antepartum depressive symptoms and postpartum depressive symptoms. Interventions aimed at detecting and managing sleep quality and elevated anxiety among depressed women in pregnancy warrant further investigation as preventative strategies for postpartum depression.

Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning.

CONTEXT: Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking. OBJECTIVES: This work aimed to establish effective models to predict early GDM. METHODS: Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively. RESULTS: A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66). CONCLUSIONS: We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

Controlling dynamic SERS hot spots on a monolayer film of Fe3O4@Au nanoparticles by a magnetic field.

A large surface-enhanced Raman scattering (SERS) effect is critically dependent on the gap distance of adjacent nanostructures, i.e., "hot spots". However, the fabrication of dynamically controllable hot spots still remains a remarkable challenge. In the present study, we employed an external magnetic field to dynamically control the interparticle spacing of a two-dimensional monolayer film of Fe3O4@Au nanoparticles at a hexane/water interface. SERS measurements were performed to monitor the expansion and shrinkage of the nanoparticles gaps, which produced an obvious effect on SERS activities. The balance between the electrostatic repulsive force, surface tension, and magnetic attractive force allowed observation of the magnetic-field-responsive SERS effect. Upon introduction of an external magnetic field, a very weak SERS signal appeared initially, indicating weak enhancement due to a monolayer film with large interparticle spacing. The SERS intensity reached maximum after 5s and thereafter remained almost unchanged. The results indicated that the observed variations in SERS intensities were fully reversible after removal of the external magnetic field. The reduction of interparticle spacing in response to a magnetic field resulted in about one order of magnitude of SERS enhancement. The combined use of the monolayer film and external magnetic field could be developed as a strategy to construct hot spots both for practical application of SERS and theoretical simulation of enhancement mechanisms.